Sr. AI Engineer
New
CanadaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 3–5+ years
- Required Skills
- DockerPythonFlaskKubernetesMachine LearningFastAPICI/CDLLM
Requirements
- 3–5+ years of professional backend engineering experience with Python and frameworks such as FastAPI or Flask.
- Proven experience deploying Python applications into production environments, beyond scripts, prototypes, or academic projects.
- Strong understanding of software design patterns, backend architecture, and scalable system development.
- Solid knowledge of performance optimization, parallel processing, background jobs, and multi-threading concepts.
- Experience optimizing systems that rely on complex or resource-intensive AI models.
- Practical machine learning experience, including training, evaluating, and maintaining task-specific models.
- Familiarity with LLM integration, prompt engineering, context optimization, and AI behavior troubleshooting.
- Ability to analyze AI outputs, identify root causes of issues, and implement targeted improvements.
- Experience with background processing frameworks such as Celery or similar technologies.
- Strong testing discipline and commitment to building reliable production systems.
- Experience with monitoring, observability, and error tracking for APIs and asynchronous workflows.
- Knowledge of Docker, CI/CD practices, deployment automation, and Kubernetes is considered an asset.
Responsibilities
- Design, develop, and deploy production-grade AI-powered backend systems that are scalable, reliable, and maintainable.
- Integrate large language models (LLMs), machine learning models, and AI-driven workflows into existing product architectures.
- Build and optimize retrieval-augmented generation (RAG) pipelines using vector databases and other machine learning approaches.
- Develop clean, structured, and testable Python code following software engineering best practices.
- Design hybrid AI architectures that balance traditional machine learning approaches with LLM capabilities to optimize performance and reliability.
- Improve system performance by reducing latency, optimizing AI model execution, and making efficient architectural decisions.
- Debug complex issues across backend services, AI inference workflows, and application integrations.
- Implement strong testing practices for backend systems and AI components to ensure production quality.
- Collaborate with product, backend, and frontend engineering teams to deliver cohesive AI-powered features.
- Monitor APIs and background processing systems, improve observability, and ensure effective error reporting.
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